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Top Python Libraries to Know About in 2020

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Top Python Libraries To Know About In 2020

Python today is one of the most sought after programming languages in the world. As per Python’s Executive Summary, “Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance.”

The most advantageous facet of Python is the richness of its library sources and the myriad uses and applications of these libraries in programming. This essay is dedicated to studying some of the best Python libraries available.

Tensor Flow

Tensor Flow is a highly popular open source library built by Google and Brain Team. It is used in almost all Google projects for machine learning. Tensor Flow 

works as a computational library for writing fresh algorithms that require vast amounts of tensor operations.

Scikit-learn

Unarguably one of the most competent libraries for working with complex data, Scikit-learn is a python library associated with Numpy and SciPy. This library facilitates cross validation or the ability to use more than one metric.

Keras

Keras is one of the most revolutionary libraries in Python in that it makes it easy to express neural networks. Keras provides some of the most competent utilities for compiling models, processing datasets and more.

PyTorch

It is the largest machine learning library that permits developers to perform tensor computation, create dynamic graphs and calculate gradients automatically. Also, it offers a rich repository of APIs for solving applications related to neural networks.

Light GBM

Gradient Boosting is one of the best machine learning libraries that helps developers build new algorithms using elementary models like decision trees. This library is highly scalable and optimal for fast implementation of gradient boosting.

Eli5

This library helps overcome the problem of inaccuracy in machine learning model predictions. It is used in mathematical operations that consume less computational time and it is important when it comes to depending on other Python libraries.

SciPy

This library is built using Numpy and it is used in high-level computations in data science. It is used extensively for scientific and computations, solving differential equations, linear algebra and optimization algorithms.

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Pandas

Python Data Analysis or Pandas is another highly popular library that is crucial to a data science life cycle in a data science project. Pandas provides super fast and flexible data structures such as data frame CDs that are specifically designed to work with structured data intuitively.

There are many more libraries like Theano and Librosa that are lesser known but very very important for machine learning, the most revolutionary scientific development of our century. To know more on the subject, do peruse the DexLab Analytics website today. DexLab Analytics is a premier Machine Learning institute in Gurgaon.

 


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Why Learning Python is Important for Data Scientists Today

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Why Learning Python is Important for Data Scientists Today

Data Science is the new rage and if you are looking to make a career, you might as well choose to become a data scientist. Data Scientists work with large sets of data to draw valuable insights that can be worked upon. Businesses rely on data scientists to sieve through tonnes of data and mine out crucial information that becomes the bedrock of business decisions in the future.

With the growth of AI, machine learning and predictive analytics, data science has come to be one of the favoured career choices in the world today. It is imperative for a data scientist to know one of more programming languages from any of those available – Java, R, Python, Scala or MATLAB.

However, Data Scientists prefer Python to other programming languages because of a number of reasons. Here we delve into some of them.

Popular

Python is one of the most popular programming languages used today. This dynamic language is easy to pick up and learn and is the best option for beginners. Secondly, it interfaces with complex high performance algorithms written in Fortran or C. It is also used for web development, data mining and scientific computing, among others.

Preferred for Data Science

Python solves most of the daily tasks a data scientist is expected to perform. “For data scientists who need to incorporate statistical code into production databases or integrate data with web-based applications, Python is often the ideal choice. It is also ideal for implementing algorithms, which is something that data scientists need to do often,” says a report. 

Packages

Python has a number of very useful packages tailored for specific functions, including pandas, NumPy and SciPy. Data Scientists working on machine learning tasks find scikit-learn useful and Matplotlib is a perfect solution for graphical representation and data visualization in data science projects.

Easy to learn

It is easy to grasp and that is why not only beginners but busy professionals also choose to learn Python for their data science needs. Compared to R, this programming language shows a sharper learning curve for most people choosing to learn it.

Scalability

Unlike other programming languages, Python is highly scalable and perceptive to change. It is also faster than languages like MATLAB. It facilitates scale and gives data scientists multiple ways to approach a problem. This is one of the reasons why Youtube migrated to Python.

Libraries

Python offers access to a wide range of data science and data analysis libraries. These include pandas, NumPy, SciPy, StatsModels, and scikit-learn. And Python will keep building on these and adding to these.  These libraries have made many hitherto unsolvable problems seem easy to crack for data scientists.

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Python Community

Python has a very robust community and many data science professionals are willing to create new data science libraries for Python users. The Python community is tight-knit one and very active when it comes to finding a solution. Programmers can connect with community members over the Internet and Codementor or Stack Overflow.

So, that is why data scientists tend to opt for Python over other programming languages. This article was brought to you by DexLab Analytics. DexLab Analytics is premiere data science training institute in Gurgaon.

 


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KNN Imputer – Release Highlights for Scikit-learn 0.22

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KNN Imputer – Release Highlights for Scikit-learn 0.22

Today we are going to learn about the new feature of Scikit-learn version 0.22 called KNN Imputation. This feature now enables us to support imputation for completing missing values using k-Nearest Neighbours (KNN). To track our tutorials on other new releases from scikit-learn, read our blog here and here.

Introduction

Each sample’s missing values are imputed using the mean value from nearest neighbours found in the training set. Two samples are close if the features that are neither missing are close. By default, a Euclidean distance metric that supports missing values, nan_euclidean_distances, is used to find the nearest neighbours.

Input and Output

So, what we do first is to import libraries like NumPy and run them. Then we create as many rows as we wish to. Then we run the function KNN Imputer and we can decide how many neighbours we want. We first, as is the procedure to use scikit-learn goes, create an object and then run it. Then we can directly put the input values in imputer.fit_transform and get the output values in the form of patterns detected in the input values.

The code sheet for this tutorial is provided in a Github repository here

 

For more on this do watch the video attached herewith. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.

Watch the video here.


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5 Crucial Subsets of Artificial Intelligence

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5 Crucial Subsets of Artificial Intelligence

As simply as can be said, artificial intelligence is a machine’s ability to replicate human intelligence and accept new inputs and perform tasks on them like human beings, learning from experience. The term was coined in the 1950s but it has today come to significantly become popular in relation to large data sets and new advanced algorithms.

AI has become the most revolutionary advancement in computing science and it is powering all sectors of the economy from banking to healthcare and agriculture today. There are many sciences branching out of artificial intelligence like machine learning, deep learning, neural networks, computer vision and robotics. Let us learn a little about each of these.

Machine Learning

Machine Learning, a crucial subset of artificial intelligence, is the machine’s ability to learn from experience with no need for human intervention explicitly. It is the most widely used form of AI in the market today. Machine Learning refers to the computer programs that are fed data, learn from them and use this experience to take intelligent decisions. Machine Learning is used in analysis, fraud detection and GPS based predictions to name a few.  

Neural Networks

Neural networks are a bunch of algorithms modelled after the neural networks that make up the human brain. They are designed to absorb and assimilate and interpret sensory data through labelling or clustering row input. The patterns they sense and interpret are in the form of numerical data, a format all text, images or even sounds must be translated into for a computer to understand. They help label, cluster and classify data based on similarities in the input fed.  

Deep Learning

Deep Learning is a technique of machine learning that uses neural networks to learn up the way humans do – by example. And it does them accurately. It is the science behind driverless cars that can distinguish a lamppost from a person. Deep learning requires a large amount of labelled data sets to be able to work and effective and substantial computing power. Deep Learning finds its applications in aerospace technology, healthcare and driverless locomotives industry among others.

Robotics

A robot is a machine capable of sensing and interpreting and interacting with its environment. Robots have become much smarter and intuitive, thanks to artificial intelligence. Robotics is an interdisciplinary field of science and engineering that is powered by a consolidated science of mechanical engineering, electrical engineering, computer science, and algorithms. Robots are used in automobile manufacturing and used to move objects in space or related fields.

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Computer Vision

Computer Vision is the field of study that seeks to enable computers to “see” virtually like the human eyes do. A computer learns by labelling or classifying various objects, albeit much faster than human beings. Its goal is image classification and recognition. The Internet is inundated with pictures and photographs. In order to search for these images, the computer system needs to know what is in them. This is where the technology of computer visions comes in.

So you see how vast the scope of artificial intelligence really is. It is a science unto itself. To learn more about the science, professionals are increasingly joining artificial intelligence training institutes across the world. DexLab Analytics, the institute that brought this article to you, is a premiere artificial intelligence training institute in Gurgaon.

 


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5 Most Powerful Computer Vision Techniques in use

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5 Most Powerful Computer Vision Techniques in use

Computer Vision is one of the most revolutionary and advanced technologies that deep learning has birthed. It is the computer’s ability to classify and recognize objects in pictures and even videos like the human eye does. There are five main techniques of computer vision that we ought to know about for their amazing technological prowess and ability to ‘see’ and perceive surroundings like we do. Let us see what they are.

Image Classification

The main concern around image classification is categorization of images based on viewpoint variation, image deformation and occlusion, illumination and background clutter. Measuring the accuracy of the description of an image becomes a difficult task because of these factors. Researchers have come up with a novel way to solve the problem.

They use a data driven approach to classify the image. Instead of classifying what each image looks like in code, they feed the computer system with many image classes and then develop algorithms that look at these classes and “learn” about the visual appearance of each class. The most popular system used for image classification is Convolutional Neural Networks (CNNs).

Object Detection

Object detection is, simply put, defining objects within images by outputting bounding boxes and labels or tags for individual objects. This differs from image classification in that it is applied to several objects all at once rather than identifying just one dominant object in an image. Now applying CNNs to this technique will be computationally expensive.

So the technique used for object detection is region-based CNNs of R-CNNs. In this technique, first an image is scanned for objects using an algorithm that generates hundreds of region proposals. Then a CNN is run on each region proposal and only then is each object in each region proposal classified. It is like surveying and labelling the items in a warehouse of a store.

Object Tracking

Object tracking refers to the process of tracking or following a specific object like a car or a person in a given scene in videos. This technique is important for autonomous driving systems in self-driving cars. Object detection can be divided into two main categories – generative method and discriminative method.

The first method uses the generative model to describe the evident characteristics of objects. The second method is used to distinguish between object and background and foreground.

Semantic Segmentation

Crucial to computer vision is the process of segmentation wherein whole images are divided or segmented into pixelgroups that are subsequently labeled and classified.

The science tries to understand the role of each pixel in the image. So, for instance, besides recognizing and detecting a tree in an image, its boundaries are depicted as well. CNNs are best used for this technique.

Instance Segmentation

This method builds on semantic segmentation in that instead of classifying just one single dominant object in an image, it labels multiple images with different colours.

When we see complicated images with multiple overlapping objects and different backgrounds, we apply instance segmentation to it. This is done to generate pixel studies of each object, their boundaries and backdrops.

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Conclusion

Besides these techniques to study and analyse and interpret images or a series of images, there are many more complex techniques that we have not delved into in this blog. However, for more on computer vision, you can peruse the DexLab Analytics website. DexLab Analytics is a premiere Deep Learning training institute In Delhi.

 


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Libraries In The Era of Artificial Intelligence

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Libraries In The Era of Artificial Intelligence

Artificial Intelligence has entered our homes and our workplaces in more ways than one. From our email services to smart vacuum cleaners and more, AI has made life easier and smoother for us. It is no surprise then that libraries, the most crucial resources we have for research, are embracing the powers of AI to streamline the vast repository of material housed by them. Here is a list of applications of AI in the library ecosystem the world over.

Expert Systems

Expert Systems are knowledge based computer systems that play a role as intelligence interfaces for providing access to a database or knowledge system. Libraries can use Expert Systems to facilitate reference services to users and members. For instance, ES provide recommendations to researches looking up a particular question.

Pointer is a very successful application in the area of reference work. It is not a knowledge board system but a computer assisted reference program. Tools like Plexus used widely in public libraries facilitate information retrieval about subject areas, reference books and more. ES are also widely used in cataloguing, indexing and classification of material in libraries across the world.

NLP

Natural Language Processing, a very crucial aspect of Artificial Intelligence, might seem like it is meant for speaking into machines and expecting them to process our words and translate them into textual matter. But NLP has more to it than just this.

Clever low-level natural language processing techniques can permit the use of free-text queries in large information retrieval systems; however, until semantic and pragmatic processing are feasible, difficult problems remain inadequately matching the true subject content of queries with that of document surrogates and documents themselves, says a report.

Robotics

As libraries continue to provide a vast cache of reference material and digital resources, they also continue to receive large amounts of printed material. This combined pressure of providing digital and printed resources to members has led to a major space constraint in libraries across the world. The goal of the Comprehensive Access to Printed Material (CAPM) is to build a robotic, on-demand and batch-scanning system that will allow for real time browsing of printed material through a web interface, says a report. After a user activates the CAPM system, it will initiate a robot to retrieve the requested item and provide it to the person making a request.

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However, despite these advantages and more, very few libraries are properly adopting technological advancements in a digital era. “So far, AI’s potential has remained largely untapped among research libraries. A recent Ex Libris survey revealed that while nearly 80 per cent of research librarians are exploring the use of AI and machine learning, only about 5 per cent are currently leveraging the technology,” says a report.

The reasons behind this trend are budgetary problems and the fear of making the post of a librarian obsolete among others. Irrespective of these reasons, it can be safe to assume that AI is the future of the library system. For more on this, do peruse the DexLab Analytics website today. Dexlab Analytics is premier institute offering natural language processing course in Gurgaon.


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Machine Learning Algorithms – With Python (Part II)

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Machine Learning Algorithms – With Python (Part II)

In the first part of this blog, we covered Parametric and Non-Parametric Machine Learning algorithms and Supervised and Unsupervised Machine Learning Algorithms. If you haven’t gone through it yet, check it out here: dexlabanalytics.com/blog/machine-learning-algorithms-with-python-part-i

In this blog we are going learn about Semi Supervised Machine Learning algorithms.

What are Semi Supervised ML algorithms?

Those algorithms in which only half of the historical data’s target data has been specified are called semi-supervised algorithms. The way to go about solving this is by making a model on the basis of the portion of historical data that has the target specified and then apply this model to the rest of the data to predict the outcomes. Now, combine the two sets of data, get the target variable and make a model on the basis of this target variable.

New Nomenclature

In the equation Y= B0 + B1X, Y is called the Target Variable while in statistics it is called the Dependent Variable. And X is called Features or Attributes whereas in statistics it is called Independent Variable. B0 and B1 are called Weights while in statistics they are called Coefficients (Intercept and Slope, respectively).

In the equation Ÿ – Y = error, the error in statistics is called Residual but in Machine Learning it is called Cost Function. And the elements of the historical data set that in statistics are known as Records or Observations, in machine learning are known as Instances.

What is Bias Variance Trade-Off?

In parametric algorithms like linear regressions, several assumptions are made before building a model. These assumptions can be things like having only those inputs that have a relationship with the target variable or the fact that the error should be random.  The benefit of this process is the fact that Ÿ or the predicted results are consistent and there is not much variance in them.

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Now, if we are to take a Decision Tree or any other non-parametric Machine Learning algorithm, a small change in the data set forces a large variance in the Target variable. But, unlike in parametric ML algorithms, there are no basic assumptions in non-parametric assumptions. So, in such a case, the error or mean square error, is a combination of the square of bias and variance.

MSE = Bias2 + Variance

Increasing any one (the square of the bias) will lead to a decrease in the other (variance) and vice versa.

In this case, we need to balance or trade off the two – the square of the bias and the variance.

While the bias cannot be changed much, we can control the variance by increasing or decreasing the parameters of the experiment.

What is Overfitting and Underfitting?

Overfitting is the condition when the accuracy figure of the ‘trained’ data set is larger in number than the accuracy figure of the ‘tested’ unseen data set. This is an undesirable condition. Underfitting is the opposite wherein the accuracy figure of the trained data is lower than that of the tested unseen data. This is also undesirable. What we seek to aim at is an equal accuracy in both the tested and trained models.

To limit Overfitting we must –

  • Use a resampling technique to estimate model accuracy by repeating experiments with the data and then drawing an average of the accuracy figures.
  • Hold back a validation data set to test your model on and increase the number of models to experiment on the trained data set.

We would like to conclude out second part of this tutorial here. For more on this, visit the third blog on Machine Learning Algorithms with Python.

(Translated from 28:00 – 1:19:00)

 


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How AI Powers The Food Processing Industry

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How AI Powers The Food Processing Industry

Can computers understand food? Can they smell aromas or taste flavours? Well, with Artificial Intelligence (AI) taking the world by storm, the food industry is not outside the purview of AI’s midas touch. In fact, AI is expected to spur the industry on to the path of growth and expansion.

According to some sources, AI in the food and beverages market is expected to register a CAGR of 28.64 percent, during the forecast period 2018-2023. According to others, today, the Food Product and Handling industry is capped at a whopping $100 billion and will continue to grow at a CAGR of 5% at least till 2021.

Here are ways in which AI is fostering the highest standards of processing and handling of food products across the world.

Sorting

One of the most important tasks in a food-processing unit is sorting. Sorting fresh produce by size, colour and quality is the first thing to be carried out and it is time consuming. For instance, sorting potatoes by size and colour will determine whether a food giant will get French fries, hash browns or chips made out of them. Herein comes the role of AI powered machines. Companies like TOMRA Sorting Food have developed sensor-based optical sorting solutions with machine learning capabilities that use cameras and near-infrared sensors to “view food in the same way that consumers do” and sort it based on that perception, says a report. This results in fewer hours spent on manual sorting, higher yields, less wastage and better quality of prepared food.

Managing Supply Chain

With newer food safety regulations being introduced ever so often and a need for transparency growing by the day, it has become imperative for food and beverage companies to put in place robust supply chain management. There are several ways in which this is being done including food safety monitoring and testing of product at every stage of the supply chain and accurate forecasting to manage pricing and inventory.

Personal Hygiene Maintenance

Maintenance of personal hygiene for everyone entering and exiting a food-processing unit is of utmost importance. In 2017, tech company Kankan signed a big deal to provide AI-powered solution for improvement of personal hygiene among workers of food processing units in China. It uses face recognition technology to detect if workers are violating rules that ensure they wear masks and caps to maintain proper hygiene at work. According to Kankan this technology is accurate by over 95 per cent.

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Cleaning processing equipment

This process is time consuming and essential to the supply chain. However, researchers are using AI to come up with better technology to reduce time taken and resources spent on cleaning equipment. For instance, researchers at the University of Nottingham have been developing a system that uses AI to reduce and cut down cleaning time and resources by 20-40 per cent. The system known as self-optimising-clean-in-place uses ultrasonic sensing and optical fluorescence imaging to detect food residue and microbial debris in equipment and facilitate cleaning of the same.

Thus, the importance of AI in various sectors of the economy cannot be stressed enough. For more on how AI powers the IT industry, read DexLab Analytics’ blog here and to know more on how AI powers space exploration read its blog here. DexLab Analytics is a premier institute offering artificial intelligence certification in Delhi NCR.

 


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DexLab Analytics Rated One of The Best Institutes in India

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DexLab Analytics Rated One of The Best Institutes in India

Analytics India Magazine (AIM), one of the foremost journals on big data and AI in India, has rated Dexlab Analytics’ credit risk modelling course one of the best in India and recommended it be taken up to learn the subject in 2020. Dexlab Analytics is on AIM’s list of nine best online courses on the subject.

In an article, the AIM has rated DexLab Analytics as a premier institute offering a robust course in credit risk modelling. Credit risk modelling is “the analysis of the credit risk that helps in understanding the uncertainty that a lender runs before lending money to borrowers”.

The article describes the Dexlab Analytics course as offering learners “an opportunity to understand the measure of central tendency theorem, measures of dispersion, probability theory and probability distribution, sampling techniques, estimation theory, types of statistical tests, linear regression, logistic regression. Besides, you will learn the application of machine learning algorithms such as Decision tree, Random Forest, XGBoost, Support Vector Machine, banking products and processes, uses of the scorecard, scorecard model development, use of scorecard for designing business strategies of a bank, LGD, PD, EAD, and much more.”

The other bodies offering competent courses on the subject on AIM’s list are Udemy, SAS, Redcliffe Training, EDUCBA, Moneyweb CPD HUB, 365 DataScience and DataCamp.

Analytics India Magazine chronicles technological progress in the space of analytics, artificial intelligence, data science & big data by highlighting the innovations, players, and challenges shaping the future of India through promotion and discussion of ideas and thoughts by smart, ardent, action-oriented individuals who want to change the world.

Since 2012, Analytics India Magazine has been dedicated to passionately championing and promoting the analytics ecosystem in India. We have been a pre-eminent source of news, information and analysis for the Indian analytics ecosystem, covering opinions, analysis, and insights on key breakthroughs and future trends in data-driven technologies as well as highlighting how they’re being leveraged for future impact.

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Dexlab Analytics has been thriving as one of the prominent institutes offering the best selection of courses on Big Data Hadoop, R Programming, Python, Business Analytics, Data Science, Machine Learning, Deep Learning, Data Visualization using Tableau and Excel. Moreover, it aims to achieve Corporate Training Excellence with each training it conducts.

For more information on this, click here – www.prlog.org/12826797-dexlab-analytics-listed-as-one-of-the-best-institutes-in-india.html

 


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